Both the idea and technology for connecting sensors and actuators to a network to remotely monitor and control physical systems have been known for many years and developed accordingly. However, a little more than a decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This paper presents the changes that the IoT has undertaken since its conception and research on how technological advances have shaped it and fostered the arising of derived names suitable to specific domains. A two-step literature review through major publishers and indexing databases was conducted; first by searching for proposals on the Internet of Things concept and analyzing them to find similarities, differences, and technological features that allow us to create a timeline showing its development; in the second step the most mentioned names given to the IoT for specific domains, as well as closely related concepts were identified and briefly analyzed. The study confirms the claim that a consensus on the IoT definition has not yet been reached, as enabling technology keeps evolving and new application domains are being proposed. However, recent changes have been relatively moderated, and its variations on application domains are clearly differentiated, with data and data technologies playing an important role in the IoT landscape.

This study evaluates the effect of conversion between tacit and explicit knowledge, and its influence on Software engineering and Software Process Improvement in the context of a small school software company in which undergraduate students participate as personnel. A survey measurement instrument was applied to the last three generations of students. The effect was measured from an interaction of the four modes of the SECI model knowledge conversion using regression analysis associated with four hypotheses study. The findings show that students are able to generate tacit and explicit knowledge in a similar way to software organizations. This study is considered a contribution of both academia and software industry that encourages this type of experiences in undergraduate students and prepares them as intellectual capital with an organizational culture that shares knowledge.

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

RESUMENLa Elicitación de Requisitos implica un proceso de entendimiento de las necesidades funcionales y las restricciones (Requisitos No Funcionales -RNF) establecidas por los stakeholders, las cuales serán entrada para las demás etapas del desarrollo de software. En dicha elicitación, la inclusión de los RNF depende de las estrategias para gestionar el conocimiento de los interesados (stakeholders y elicitadores) con el objetivo de generar productos software de alta calidad. Este documento se centra en establecer los diferentes flujos de transformación del conocimiento que se generan en el proceso de Elicitación de RNF al integrar diferentes componentes de la gestión del conocimiento. El núcleo de transformación del conocimiento propuesto fue valorado mediante las técnicas Focus Group y reporte de experiencia obteniendo resultados positivos para su implementación dentro de las prácticas de las organizaciones dedicadas al desarrollo de software.Palabras clave: Ingeniería de software, elicitación de requisitos, gestión del conocimiento, requisitos no funcionales. (Non-Functional Requirements -NFR) ABSTRACT Requirements elicitation involves a process of understanding of the functional requirements and restrictions

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic strategies are known to halt disease progression or reverse muscle weakness or atrophy. Many genes may be incorrectly regulated in affected muscle tissue, but the mechanisms responsible for the progressive muscle weakness remain largely unknown. Although machine learning (ML) has made significant inroads in biomedical disciplines such as cancer research, no reports have yet addressed FSHD analysis using ML techniques. This study explores a specific FSHD data set from a ML perspective. We report results showing a very promising small group of genes that clearly separates FSHD samples from healthy samples. In addition to numerical prediction figures, we show data visualizations and biological evidence illustrating the potential usefulness of these results.